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Reseach Article

Nonlinear Regression using Particle Swarm Optimization and Genetic Algorithm

by Pakize Erdoğmuş, Simge Ekiz
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 6
Year of Publication: 2016
Authors: Pakize Erdoğmuş, Simge Ekiz
10.5120/ijca2016912081

Pakize Erdoğmuş, Simge Ekiz . Nonlinear Regression using Particle Swarm Optimization and Genetic Algorithm. International Journal of Computer Applications. 153, 6 ( Nov 2016), 28-36. DOI=10.5120/ijca2016912081

@article{ 10.5120/ijca2016912081,
author = { Pakize Erdoğmuş, Simge Ekiz },
title = { Nonlinear Regression using Particle Swarm Optimization and Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 153 },
number = { 6 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume153/number6/26407-2016912081/ },
doi = { 10.5120/ijca2016912081 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:58:25.161560+05:30
%A Pakize Erdoğmuş
%A Simge Ekiz
%T Nonlinear Regression using Particle Swarm Optimization and Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 153
%N 6
%P 28-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nonlinear regression is a type of regression which is used for modeling a relation between the independent variables and dependent variables. Finding the proper regression model and coefficients is important for all disciplines. In this study, it is aimed at finding the nonlinear model coefficients with two well-known population-based optimization algorithms. Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) were used for finding some nonlinear regression model coefficients. It is shown that both algorithms can be used as an alternative way for coefficients estimation of nonlinear regression models.

References
  1. Kumar, T. (2015, February). Solution of Linear and Non Linear Regression Problem by K Nearest Neighbor Approach: By Using Three Sigma Rule. Computational Intelligence & Communication Technology (CICT), pp. 197-201. doi: 10.1109/CICT.2015.110
  2. Akoa, B. E., Simeu, E., and Lebowsky, F. (2013, July). Video decoder monitoring using nonlinear regression. 2013 IEEE 19th International On-Line Testing Symposium (IOLTS), pp. 175-178. doi: 10.1109/IOLTS.2013.6604073G.
  3. Lu, F., Sugano, Y., Okabe, T., and Sato, Y. (2011, November). Inferring human gaze from appearance via adaptive linear regression. 2011 International Conference on Computer Vision, pp.153-160. doi: 10.1109/ICCV.2011.6126237
  4. Martinez, F., Carbone, A., and Pissaloux, E. (2012, September). Gaze estimation using local features and nonlinear regression. 19th IEEE International
  5. Conference on Image Processing, pp. 1961-1964. doi: 0.1109/ICIP.2012.6467271
  6. Venkataraman, S., and Gorur, R. S. (2006). Non linear regression model to predict flashover of nonceramic insulators. 38th Annual North American Power Symposium, NAPS-2006, pp. 663-666.doi: 10.1109/NAPS.2006.359643
  7. Frecon, J., Fontugne, R., Didier, G., Pustelnik, N., Fukuda, K., and Abry, P. (2016, March). Nonlinear regression for bivariate self-similarity identification— application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4184-4188.
  8. Gray, R. A., Docherty, P. D., Fisk, L. M., and Murray, R. (2016). A modified approach to objective surface generation within the Gauss-Newton parameter identification to ignore outlier data points. Biomedical Signal Processing and Control, 30, pp. 162-169. http://dx.doi.org/10.1016/j.bspc.2016.06.009.
  9. Lu, Z., Yang, C., Qin, D., Luo, Y., and Momayez, M. (2016). Estimating ultrasonic time-of-flight through echo signal envelope and modified Gauss Newton method. Measurement, 94, pp. 355-363. http://dx.doi.org/10.1016/j.measurement.2016.08.013
  10. Nonlinear Least Squares Regression. (n.d.). Engineering Statistics Handbook. Retrieved from http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd142.htm
  11. Minot, A., Lu, Y. M., and Li, N. (2015). A distributed Gauss-Newton method for power system state estimation. IEEE Transactions on Power Systems, 31(5), pp. 3804-3815. doi: 10.1109/TPWRS.2015.2497330
  12. Polykarpou, E., and Kyriakides, E. (2016, April). Parameter estimation for measurement-based load modeling using the Levenberg-Marquardt algorithm. Electrotechnical Conference (MELECON), 2016 18th Mediterranean, pp. 1-6. doi: 10.1109/MELCON.2016.7495363
  13. Lourakis, M. L. A., and Argyros, A. A. (2005, October). Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment?. Tenth IEEE International Conference on Computer Vision (ICCV'05, 1, pp. 1526-1531). doi: 10.1109/ICCV.2005.128.
  14. Basics on Continuous Optimization, (2011, July). Retrieved from  http://www.brnt.eu/phd/node10.html
  15. Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press, Retrieved from https://books.google.com.tr/books?id=YE5RAAAAMAAJ&redir_esc=y
  16. Ijjina, E. P., and Chalavadi, K. M. (2016). Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recognition,59, pp. 199-212. http://dx.doi.org/10.1016/j.patcog.2016.01.012.
  17. Bamakan, S. M. H., Wang, H., and Ravasan, A. Z. (2016). Parameters Optimization for Nonparallel Support Vector Machine by Particle Swarm Optimization. Procedia Computer Science, 91, pp. 482-491. http://dx.doi.org/10.1016/j.procs.2016.07.125
  18. Liu, F., and Zhou, Z. (2015). A new data classification method based on chaotic particle swarm optimization and least square-support vector machine. Chemometrics and Intelligent Laboratory Systems, 147, pp. 147-156. http://dx.doi.org/10.1016/j.chemolab.2015.08.015.
  19. Cavuslu, M. A., Karakuzu, C., and Karakaya, F. (2012). Neural identification of dynamic systems on FPGA with improved PSO learning. Applied Soft Computing, 12(9), pp.27072718.http://dx.doi.org/10.1016/j.asoc.2012.03.022.
Index Terms

Computer Science
Information Sciences

Keywords

Nonlinear regression Genetic Algorithm Particle Swarm Optimization